# Multiscale networks in Alzheimer’s disease identify brain hypometabolism as central across biological scales

## TL;DR

This study uses a systems biology approach to show that brain hypometabolism is a central feature of Alzheimer’s disease across multiple biological scales.

## Contribution

The study introduces a multilayer network analysis method to integrate diverse data types and identify key biological paths in Alzheimer’s disease.

## Key findings

- Brain hypometabolism, particularly in the posterior cingulate, is a central feature in Alzheimer’s disease.
- Multilayer network analysis reveals paths involving mental health and vascular factors that influence disease phenotypes.
- Energy deficit in brain networks plays a significant role in the progression of Alzheimer’s disease.

## Abstract

Alzheimer’s disease encompasses multiple biological scales, spanning molecular factors, cells, tissues, and behavioral manifestations. The interplay among these scales in shaping the clinical phenotype is not yet fully comprehended. In particular, there is great interest in understanding the heterogeneity of the clinical aspects of AD in order to improve treatment and prevention, by targeting those aspects most susceptible to the disease. Here we employed a systems biology approach to address this issue, utilizing multilayer network analysis and deep phenotyping. This integrative analysis incorporated genomics, cerebrospinal fluid biomarkers, tau and amyloid beta (Aβ) PET imaging, brain MRI data, risk factors, and clinical information (cognitive tests scores, Clinical Dementia Rating and clinical diagnosis) obtained through the ADNI collaboration. Multilayer networks were built based on mutual information between the elements of each layer and between layers. Boolean simulations allowed us to identify paths that transmit dynamic information across layers. The most prominent path for predicting variables in the cognitive phenotype layer included the PET radiotracer fluorodeoxyglucose (FDG) in the posterior cingulate. Combinations of different symptomatic variables, mainly related to mental health (depression, mood swings, drowsiness) and vascular features (hypertension, cardiovascular history), were also part of the paths explaining the average phenotype. Our results show that integrating the flow of information across biological scales reveals relevant paths for AD, which can be subsequently explored as potential biomarkers or therapeutic targets. In particular, our results point for paths related with brain hypometabolism as a key feature in AD.

Complex diseases such as Alzheimer’s Disease (AD) involve a diverse array of biological processes. In our investigation, we undertook a systems biology approach to AD using network analysis and deep phenotyping within a prospective cohort of patients, incorporating clinical, imaging, genetics, and omics assessments. The gene, molecular and imaging paths explained variation in central nervous system damage, and in metrics of disease severity, pointing to a significant role of energy deficit within brain networks in the development of AD. The elucidation of multilayer paths in this context provides insights into the diverse phenotypes of the disease and holds the potential to improve understanding of its pathogenesis.